edX: Statistical Inference and Modeling for High-throughput Experiments

 with  Michael Love and Rafael Irizarry
Project Management Certificate
Cornell University via eCornell

In this course you’ll learn various statistics topics including multiple testing problem, error rates, error rate controlling procedures, false discovery rates, q-values and exploratory data analysis. We then introduce statistical modeling and how it is applied to high-throughput data. In particular, we will discuss parametric distributions, including binomial, exponential, and gamma, and describe maximum likelihood estimation. We provide several examples of how these concepts are applied in next generation sequencing and microarray data. Finally, we will discuss hierarchical models and empirical bayes along with some examples of how these are used in practice. We provide R programming examples in a way that will help make the connection between concepts and implementation.

Given the diversity in educational background of our students we have divided the series into seven parts. You can take the entire series or individual courses that interest you. If you are a statistician you should consider skipping the first two or three courses, similarly, if you are biologists you should consider skipping some of the introductory biology lectures. Note that the statistics and programming aspects of the class ramp up in difficulty relatively quickly across the first three courses. By the third course will be teaching advanced statistical concepts such as hierarchical models and by the fourth advanced software engineering skills, such as parallel computing and reproducible research concepts.

These courses make up 2 XSeries and are self-paced:

PH525.1x: Statistics and R for the Life Sciences

PH525.2x: Introduction to Linear Models and Matrix Algebra

PH525.3x: Statistical Inference and Modeling for High-throughput Experiments

PH525.4x: High-Dimensional Data Analysis

PH525.5x: Introduction to Bioconductor: annotation and analysis of genomes and genomic assays 

PH525.6x: High-performance computing for reproducible genomics

PH525.7x: Case studies in functional genomics

This class was supported in part by NIH grant R25GM114818.

HarvardX requires individuals who enroll in its courses on edX to abide by the terms of the edX honor code. HarvardX will take appropriate corrective action in response to violations of the edX honor code, which may include dismissal from the HarvardX course; revocation of any certificates received for the HarvardX course; or other remedies as circumstances warrant. No refunds will be issued in the case of corrective action for such violations. Enrollees who are taking HarvardX courses as part of another program will also be governed by the academic policies of those programs.

HarvardX pursues the science of learning. By registering as an online learner in an HX course, you will also participate in research about learning. Read our research statement to learn more.

Harvard University and HarvardX are committed to maintaining a safe and healthy educational and work environment in which no member of the community is excluded from participation in, denied the benefits of, or subjected to discrimination or harassment in our program. All members of the HarvardX community are expected to abide by Harvard policies on nondiscrimination, including sexual harassment, and the edX Terms of Service. If you have any questions or concerns, please contact and/or report your experience through the edX contact form.

3 Student
Cost Free Online Course
Pace Self Paced
Institution Harvard University
Provider edX
Language English
Certificates $49 Certificate Available
Hours 5 hours a week
Calendar 35 weeks long
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3 reviews for edX's Statistical Inference and Modeling for High-throughput Experiments

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1 out of 1 people found the following review useful
a year ago
Brandt Pence completed this course, spending 4 hours a week on it and found the course difficulty to be medium.
(Note I took these before the recent reorganization. I believe most of the material from the first few courses has remained relatively the same.) This is the third course in the PH525 sequence offered by HarvardX. This course ended up being a bit of a surprise to me, as it was far more difficult than the previous two Read More
(Note I took these before the recent reorganization. I believe most of the material from the first few courses has remained relatively the same.)

This is the third course in the PH525 sequence offered by HarvardX. This course ended up being a bit of a surprise to me, as it was far more difficult than the previous two courses (PH525.1x and PH525.2x). Whereas previously, the lectures were at a higher level than the assignments, the assignments in this course were more difficult than the material covered in the lectures, and there was quite a bit less hand- holding compared to previous courses. Part of this may have been the material, as I had a solid background in the topics covered in the previous courses (basic statistics, R programming, and regression analyses), but I have little background in multivariate analyses.

The materials covered in this course include statistical inference for high throughput data, cluster and factor analysis, principal component analysis, hierarchical modeling, and more. I needed quite a bit of help from the discussion boards to get through some of the homework problems. Fortunately, although the EdX discussion boards are relatively poor, there was sufficient information there to get through most of the problems. I found the homework to be much less intuitive compared to previous classes, but I did learn a lot of programming and analysis tricks in this class.

Overall, four stars. This has been the most difficult course I've taken to this point, but getting the right answers is rewarding, and the instructors have set up the homeworks so that you have sufficient attempts to get the right answer (barely, in some cases).
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2 years ago
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2 years ago
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